基于WCVaR模型的分布式发电系统供需互动能量管理研究
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摘要
为提高新能源发电利用水平以及衡量其发电不确定性带来的风险,该文以分布式发电系统供需协调互动作为提升系统运行经济性与消纳可再生能源的手段,并针对系统内可再生分布式电源出力的波动性,引入最差条件风险价值(worst-case conditional value-at-risk,WCVaR)理论,构建了包含分布式电源运行成本、需求侧互动成本和交互收益的能量管理风险模型,量化其发电不确定性对系统进行能量交易时产生的收益风险,将风险水平限制在可接受的前提下,追求系统收益最大。在随机变量服从离散界约束分布的条件下,运用拉格朗日对偶理论将复杂的min-max结构转化为确定性半定规划进行求解。仿真算例表明,在随机变量概率分布信息部分已知的情况下,该模型能有效规避新能源发电出力不确定性造成的收益波动风险,对可再生能源发电接入系统的能量管理研究问题提供了理论指导。
In order to improve the utilization level of new energy power generation and to measure the risk brought by its uncertainty. This paper used supply and demand coordination as a means to enhance system operation economy and to absorb renewable energy. Aiming at output fluctuation and uncertainty of renewable distributed power, the worst conditional value at risk(WCVaR) theory was introduced in order to evaluate profit risk in system energy trading. A benefit risk model of energy management including operation cost of distributed power,interactive cost of demand side and mutual benefit was developed for energy management system, and the objective was to maximize system profit and to limit risk with an acceptable level. When random variables obeyed the discrete bounding constraint distribution, the complex min-max structure was transformed to a deterministic semi definite programming solved by the Lagrange duality theory.Simulation results show that the proposed model can effectively avoid return fluctuation risk caused by new energy output uncertainty, meanwhile it provides the theoretical guidance for energy management research of the renewable energy generation system.
In order to improve the utilization level of new energy power generation and to measure the risk brought by its uncertainty. This paper used supply and demand coordination as a means to enhance system operation economy and to absorb renewable energy. Aiming at output fluctuation and uncertainty of renewable distributed power, the worst conditional value at risk(WCVaR) theory was introduced in order to evaluate profit risk in system energy trading. A benefit risk model of energy management including operation cost of distributed power,interactive cost of demand side and mutual benefit was developed for energy management system, and the objective was to maximize system profit and to limit risk with an acceptable level. When random variables obeyed the discrete bounding constraint distribution, the complex min-max structure was transformed to a deterministic semi definite programming solved by the Lagrange duality theory.Simulation results show that the proposed model can effectively avoid return fluctuation risk caused by new energy output uncertainty, meanwhile it provides the theoretical guidance for energy management research of the renewable energy generation system.
引文
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[14]刘扬洋,蒋传文,谭胜敏,等.考虑风险调整资本收益率阈值约束的虚拟电厂优化调度策略[J].中国电机工程学报,2016,36(17):4617-4627.Liu Yangyang,Jiang Chuanwen,Tan Shengmin,et al.Optimal dispatch of virtual power plant considering risk adjusted return on capital constraints[J].Proceedings of the CSEE,2016,36(17):4617-4627(in Chinese).
[15]Rezaei N,Kalantar M.Stochastic frequency-security constrained energy and reserve management of an inverter interfaced islanded microgrid considering demand response programs[J].International Journal of Electrical Power&Energy Systems,2015(69):273-286.
[16]Cese?a E A M,Mancarella P.Distribution network support from multi-energy demand side response in smart districts[C]//Innovative Smart Grid TechnologiesAsia.Melboure,Australia:IEEE,2016:753-758.
[17]Bitaraf H,Rahman S.Reducing curtailed wind energy through energy storage and demand response[J].IEEETransactions on Sustainable Energy,2019,9(1):228-236.
[18]Rockfellar R T.Optimization of conditional value-at risk[J].Journal of Risk,2000,2(1):1071-1074.
[19]Zhu S,Fukushima M.Worst-case conditional value-at-risk with application to robust portfolio management[J].Operations Research,2009,57(5):1155-1168.
[20]Le D D,Gross G,Berizzi A.Probabilistic modelling of multisite wind farm production for scenario-based applications[J].IEEE Transactions on Sustainable Energy,2015,6(3):748-758.
[2]Eajal A A,Shaaban M F,Ponnambalam K,et al.Stochastic centralized dispatch scheme for AC/DChybrid smart distribution systems[J].IEEE Transactions on Sustainable Energy,2017,7(3):1046-1059.
[3]Elnozahy M S,Salama M M A.Uncertainty-based design of a bilayer distribution system for improved integration of PHEVs and PV arrays[J].IEEE Transactions on Sustainable Energy,2015,6(3):659-674.
[4]陈国平,李明节,许涛,等.我国电网支撑可再生能源发展的实践与挑战[J].电网技术,2017,41(10):3095-3103.Chen Guoping,Li Mingjie,Xu Tao,et al.Practice and challenge of renewable energy development based on interconnected power grids[J].Power System Technology,2017,41(10):3095-3103(in Chinese).
[5]杨晓东,张有兵,赵波,等.供需两侧协同优化的电动汽车充放电自动需求响应方法[J].中国电机工程学报,2017,37(1):120-130.Yang Xiaodong,Zhang Youbing,Zhao Bo,et al.Automated demand response method for electric vehicles charging and discharging to achieve supplydemand coordinated optimization[J].Proceedings of the CSEE,2017,37(1):120-130(in Chinese).
[6]田世明,王蓓蓓,张晶.智能电网条件下的需求响应关键技术[J].中国电机工程学报,2014,34(22):3576-3589.Tian Shiming,Wang Beibei,Zhang Jing.Key technologies for demand response in smart grid[J].Proceedings of the CSEE,2014,34(22):3576-3589(in Chinese).
[7]王蓓蓓,唐楠,赵盛楠,等.需求响应参与风电消纳的随机&可调节鲁棒混合日前调度模型[J].中国电机工程学报,2017,37(21):6339-6346.Wang Beibei,Tang Nan,Zhao Shengnan,et al.Stochastic&adjustable robust hybrid scheduling model of power system considering demand response’s participation in large-scale wind power consumption[J].Proceedings of the CSEE,2017,37(21):6339-6346(in Chinese).
[8]刘文学,梁军,贠志皓,等.考虑节能减排的多目标模糊机会约束动态经济调度[J].电工技术学报,2016,31(1):62-70.Liu Wenxue,Liang Jun,Yun Zhihao,et al.Multi-objective fuzzy chance constrained dynamic economic dispatch considering energy saving and emission reduction[J].Transactions of China Electrotechnical Society,2016,31(1):62-70(in Chinese).
[9]范松丽,艾芊,贺兴.基于机会约束规划的虚拟电厂调度风险分析[J].中国电机工程学报,2015,35(16):4025-4034.Fan Songli,Ai Qian,He Xing.Risk analysis on dispatch of virtual power plant based on chance constrained programming[J].Proceedings of the CSEE,2015,35(16):4025-4034(in Chinese).
[10]夏榆杭,刘俊勇,冯超,等.计及需求响应的虚拟发电厂优化调度模型[J].电网技术,2016,40(6):1666-1674.Xia Yuhang,Liu Junyong,Feng Chao,et al.Optimal scheduling model of virtual power plant considering demand response[J].Power System Technology,2016,40(6):1666-1674(in Chinese).
[11]Zhou Y,Mancarella P,Mutale J.Modelling and assessment of the contribution of demand response and electrical energy storage to adequacy of supply[J].Sustainable Energy Grids&Networks,2015,3(3):12-23.
[12]Aghaei J,Alizadeh M I.Robust n-k contingency constrained unit commitment with ancillary service demand response program[J].IET Generation Transmission&Distribution,2014,8(12),1928-1936.
[13]Tan Z,Wang G,Ju L,et al.Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand response[J].Energy,2017(124):198-213.
[14]刘扬洋,蒋传文,谭胜敏,等.考虑风险调整资本收益率阈值约束的虚拟电厂优化调度策略[J].中国电机工程学报,2016,36(17):4617-4627.Liu Yangyang,Jiang Chuanwen,Tan Shengmin,et al.Optimal dispatch of virtual power plant considering risk adjusted return on capital constraints[J].Proceedings of the CSEE,2016,36(17):4617-4627(in Chinese).
[15]Rezaei N,Kalantar M.Stochastic frequency-security constrained energy and reserve management of an inverter interfaced islanded microgrid considering demand response programs[J].International Journal of Electrical Power&Energy Systems,2015(69):273-286.
[16]Cese?a E A M,Mancarella P.Distribution network support from multi-energy demand side response in smart districts[C]//Innovative Smart Grid TechnologiesAsia.Melboure,Australia:IEEE,2016:753-758.
[17]Bitaraf H,Rahman S.Reducing curtailed wind energy through energy storage and demand response[J].IEEETransactions on Sustainable Energy,2019,9(1):228-236.
[18]Rockfellar R T.Optimization of conditional value-at risk[J].Journal of Risk,2000,2(1):1071-1074.
[19]Zhu S,Fukushima M.Worst-case conditional value-at-risk with application to robust portfolio management[J].Operations Research,2009,57(5):1155-1168.
[20]Le D D,Gross G,Berizzi A.Probabilistic modelling of multisite wind farm production for scenario-based applications[J].IEEE Transactions on Sustainable Energy,2015,6(3):748-758.